基于多元线性回归的线性系统状态空间辨识算法

提出了一种新的多变量线性系统状态空间辨识算法.该算法采用多元线性回归,而不是传统算法中的子空间投影.首先通过多元线性回归获得系统的预估器马尔可夫参数,然后基于一个关鍵等式获得系统的预估器可观性矩阵与状态序列的乘积矩阵,接着通过奇异值分解得到状态序列,最终再次运用多元线性回归求得系统状态空间模型的各个矩阵.由于本文的算法是预估器式的,因此适用于开环和闭环辨识.基于AIC准则,设计了算法的阶次选择策略,通过仿真例子,验证了该算法的有效性.     

基于支持向量机的物流量预测——以成都市为例

考虑到支持向量机强大的泛化能力,运用支持向量机技术预测了成都市近期和中期物流量.研究表明,随着样本数据的增加,支持向量机的预测值与实际值间的相对误差总体呈下降趋势,并且与多元线性回归方法相比,支持向量机具有较高的预测精度,证明了支持向量机用于物流量预测的有效性.     

预应力混凝土连续钢构桥施工要点

依据工程实际,分析了大体积承台混凝土冬季防裂施工、双薄壁空心超高墩施工、超高墩大跨悬灌线型控制施工、湿陷性黄土地基高边墩边跨现浇段施工的施工特点,论述了预应力混凝土连续钢构桥施工步骤与基本方法.     

灾害能量链式演化与介质载体形态变化关系

以地质灾害能量质环理论为研究导向,分析自然灾害诱发机理与介质载体形态变化关系,从能量学角度探索介质载体形态变化特征以及对灾害产生整个过程中所呈现出的表观形态,为自然灾害预警提供一定的理论指导。     

基于飞思卡尔32位Kinetis-K60单片机的直立行驶智能车设计

介绍一种基于线性CCD传感器进行路径识别的智能车控制系统。进行了软硬件设计,利用线性CCD传感器采集路径信息,以及陀螺仪和加速度计采集角度偏转信息,提出转向控制策略和速度控制策略,控制2个电机的动作,从而实现了直立行驶。实验证明:该智能车抗干扰性强、精确度较高,可自主寻迹稳定行驶。     

矮塔斜拉桥施工阶段线形敏感性分析

文章以某双塔单索面预应力混凝土矮塔斜拉桥为工程背景,研究矮塔斜拉桥施工阶段线形对结构自重偏差、混凝土受力龄期、系统温差、索塔温度梯度、主梁梁温度梯度、索梁温差、斜拉索张拉力偏差、主梁预应力张拉力偏差等参数的敏感性,并引入敏感性评价参数α,合理评价各种参数对施工阶段桥梁线形的影响程度。研究结果表明:温度作用对施工阶段桥梁线形影响较大,矮塔斜拉桥的施工监控应加强温度监测;主梁自重偏差对施工阶段桥梁线形也影响较大,施工中应严格控制混凝土方量误差。     

层次分析法在泊车诱导中的应用

通过层次分析法分析涉及停车场运营的泊车路径规划、上下层泊位选取、泊车效率、历史泊车时问等因素对？白位经济性的贡献程度,做出各种诱导费用方案并通过线性加权计算。比较结果后做出选择,以达到该类自动化立体停车库的运营成本最低,在路径规划计算上引入Lee算法,进行最短路径求解。     

偏好泊位约束下的集装箱港口离散泊位分配问题*

在集装箱码头中,泊位调度系统是影响码头作业调度的重要环节,而泊位分配问题又是其首要问题。为了揭示 泊位偏好对泊位分配问题的影响,重点从集装箱码头泊位分配问题和基于船舶泊位偏好问题两方面展开研究,为对泊位分 配和泊位偏好进行协同优化配置,建立泊位偏好约束下的多目标混合整数数学模型。并且采用CPLEX进行求解。通过算例 说明模型的有效性,并通过实验分析船舶对泊位的偏好的影响。结果对港口具有的实际应用价值,拓展了对离散型靠泊计 划的研究。     

Daily newspaper distribution planning with integer programming: an application in Turkey

Abstract

This paper concerns the newspaper distribution problem. It addresses the transportation of newspapers from printing plant to newsagents with distribution vehicles under various particular constraints. The objective is to minimize the distance traveled by the vehicles and/or the number of vehicles. In this study, the routes for vehicles of a leading newspaper distributor company in the Turkish press sector are examined. The problem is defined as determining optimal delivery routes for a fleet of homogeneous vehicles, starting and ending at the printing plant that is required to serve a number of geographically dispersed newsagents with known demands under capacity and time constraints, while minimizing the total distribution cost. An integar linear programming model is proposed as a solution using Cplex. Computational results demonstrate that the proposed model is fast and able to find optimal solutions for problem scenarios with up to 55 newsagents within reasonable computing times. It was found that the proposed model reduced the delivery cost by 21% on average when compared to the current manual method. The results show that this model is adequate for medium-sized distribution problems.     

An adaptive neural network-fuzzy linear regression approach for improved car ownership estimation and forecasting in complex and uncertain environments: the case of Iran

This paper applies a novel adaptive approach consisting of Artificial Neural Network (ANN) and Fuzzy Linear Regression (FLR) to improve car ownership forecasting in complex, ambiguous, and uncertain environments. This integrated approach is applied to forecast car ownership in Iran from 1930 to 2007. In this study, the level of car ownership is viewed as the result of demographic, politico-social, and urban structure factors including average family size, total population density, urban population density, urbanization rate, gross national product per capita, gasoline price, and total road length. To capture the potential complexity, uncertainty, and linearity relation between the car ownership function and its determinants, ANN and FLR (including eight well-known FLR) approaches are applied to the collected data. Next, the preferred ANN is selected based on sensitivity analysis results for the test data while the preferred FLR is identified with regard to ANOVA and MAPE results. The results obtained from the performance comparison demonstrate the considerable superiority of the preferred ANN over the preferred FLR regarding the nonlinear and complex nature of the car ownership function in Iran. This is the first study that presents an ANN-FLR approach for car ownership forecasting capable of handling complexity and non-linearity, uncertainty, pre-processing, and post-processing.